Dataset for solving a hybrid flexibility strategy on personnel scheduling problem in the retail industry

Published: 30-07-2020| Version 1 | DOI: 10.17632/rzf6whbmg6.1
Andrés Porto,
César Henao,
Héctor López-Ospina,
Esneyder Rafael González Ponzón,


This dataset is related with the Data in Brief article entitled: “Dataset for solving a hybrid flexibility strategy on personnel scheduling problem in the retail industry”, which was published in Data in Brief Journal. This data article describes datasets from a home improvement retail store located in Santiago, Chile. The datasets have been developed to simultaneously solve a staffing and tour scheduling problem that incorporates flexible contracts and multiskilled staff. In turn, this Data in Brief article is related to the published article "Hybrid flexibility strategy on personnel scheduling: Retail case study" (Porto et al., 2019). The datasets contain real, processed, and simulated data. Regarding the real and processed datasets, they are presented for three different store sizes (4, 5 or 6 departments). Real datasets include information about the employment-contract characteristics, cost parameters, and a forecast of the number of employees required in each department for each day of the week and each time period into which the operating day is divided. As regards to the data processed for the case study, they include the set of skill sets considering that the employees can be trained in a maximum of two store departments. Regarding the simulated datasets, they include information about the random parameter of staff demand in each store department. The simulated data are presented in 90 text files classified by: (i) Store size (4, 5 or 6 departments). (ii) Coefficient of variation (10, 20, 30%). (iii) Instance identification number (10 instances per scenario that resulted from combining the store sizes and coefficients of variation). Researchers can use the datasets for benchmarking the performance of different approaches with the one presented by Porto et al. (2019), and in consequence, they can find solutions to the same (or similar) type of personnel scheduling problem. The dataset includes an Excel workbook that can be used to randomly generate staff demand instances according to a chosen coefficient of variation.